{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T08:40:32Z","timestamp":1773736832008,"version":"3.50.1"},"reference-count":76,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,9,30]],"date-time":"2020-09-30T00:00:00Z","timestamp":1601424000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.<\/jats:p>","DOI":"10.3390\/s20195609","type":"journal-article","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T09:04:12Z","timestamp":1601543052000},"page":"5609","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":152,"title":["Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6605-498X","authenticated-orcid":false,"given":"Shahab S.","family":"Band","sequence":"first","affiliation":[{"name":"Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam"},{"name":"Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6314-6838","authenticated-orcid":false,"given":"Saeid","family":"Janizadeh","sequence":"additional","affiliation":[{"name":"Department of Watershed Management Engineering and Sciences, Faculty in Natural Resources and Marine Science, Tarbiat Modares University, 14115-111 Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0805-8007","authenticated-orcid":false,"given":"Subodh","family":"Chandra Pal","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Burdwan, West Bengal, Burdwan 713104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9032-2198","authenticated-orcid":false,"given":"Asish","family":"Saha","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Burdwan, West Bengal, Burdwan 713104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6323-4838","authenticated-orcid":false,"given":"Rabin","family":"Chakrabortty","sequence":"additional","affiliation":[{"name":"Department of Geography, The University of Burdwan, West Bengal, Burdwan 713104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manouchehr","family":"Shokri","sequence":"additional","affiliation":[{"name":"Institute of Structural Mechanics, Bauhaus Universit\u00e4t Weimar, 99423 Weimar, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4842-0613","authenticated-orcid":false,"given":"Amirhosein","family":"Mosavi","sequence":"additional","affiliation":[{"name":"Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc ThangUniversity, Ho Chi Minh City 700000, Vietnam"},{"name":"Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Keesstra, S., Mol, G., De Leeuw, J., Okx, J., De Cleen, M., Visser, S., and Molenaar, C. 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